Method for 3d reconstruction from satellite imagery

ABSTRACT

The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing ( 101 ) at least two overlapping 2D satellite images, providing ( 102 ) imaging device parameters for the at least two overlapping 2D satellite images, providing ( 103 ) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting ( 104 ) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of EuropeanApplication No. 21178195.0, filed on Jun. 8, 2021, the entire contentsof which as are hereby incorporated by reference.

The present invention relates to a method for 3D reconstruction fromsatellite imagery using deep learning.

BACKGROUND Related Field

A fast-growing market both in civilian and military business isgeographical information systems. Knowledge about geographicalconditions forms a fundamental decision support to companies,authorities and in the military. The geographical information cancomprise digital maps having superposed information layers such asinfrastructure, terrain type and different types of objects. This way ofproviding digital maps comprises forming two-dimensional maps comprisingcapturing images of the terrain from an aircraft and post-processing ofthe captured images. It is possible to form three-dimensional maps fromcaptured images or range data sets of the terrain/infrastructure.

In order to achieve an efficient capturing of images of the ground it isadvantageous to use satellite images as satellites may capture imagescovering a large area in a short time. A drawback with satellite imagesis that they have lower resolution than aerial images taken from, e.g.,an aeroplane. It is however very time consuming and sometimes impossibleto capture aerial images covering large areas, one reason being that theground areas to be captured on images are in a country not allowingaerial image capturing.

BRIEF SUMMARY

An objective of the present invention is to provide a method, a systemand a computer program, which enables more efficient and/or accurate 3Dreconstruction of large areas, than has been possible according to theprior art.

At least one of these objects is fulfilled with a method, a system and acomputer program according to the independent claims.

Further advantages are achieved with the features of the dependentclaims.

According to a first aspect of the present invention, a method isprovided for 3D reconstruction from satellite imagery using deeplearning. The method comprises providing at least two overlapping 2Dsatellite images; providing imaging device parameters for the at leasttwo 2D satellite images; providing at least one trained Machine LearningNetwork, MLN, able to predict depth maps, said trained MLN being trainedon a training set in which ground truth data comprises multi-viewgeocoded ground truth data and predicting a depth map of the provided atleast two 2D satellite images using the trained at least one MLN andbased on the corresponding imaging device parameters.

According to a second aspect of the present invention, a system isprovided for 3D reconstruction from satellite imagery using least two 2Dsatellite images and deep learning.

The advantages of such a system are the same as those described inrelation to the first aspect of the invention. Thus, the system enablesmore efficient and/or accurate 3D reconstruction of large areas.

According to a third aspect of the present invention a computer programfor 3D reconstruction from satellite imagery using deep learning,comprising instructions which, when executed by at least one processorcause the at least one processor to carry out the method according tothe first aspect or any of the preferred embodiments of the firstaspect.

BRIEF DESCRIPTION OF THE FIGURES

In the following description of preferred embodiments, reference will bemade to the attached drawings on which

FIG. 1 is a flow chart illustrating an example of a method for 3Dreconstruction from satellite imagery using deep learning.

FIG. 2 illustrates schematically examples of processing of a computerprogram for generation of data for 3D reconstruction from satelliteimagery using deep learning.

FIG. 3 is a flow chart illustrating an example of a method for traininga Machine Learning Network, MLN, for 3D reconstruction from satelliteimagery.

FIG. 4 shows schematically an illustrating example of data used forgeneration of data for 3D reconstruction from satellite images.

FIG. 5 illustrates an example of 3D reconstruction from satelliteimagery using deep learning.

FIG. 6 illustrates an example 3D reconstruction formed as a mesh.

FIG. 7 illustrates an example of a mesh uncertainty.

FIG. 8 is a block scheme illustrating an example of a system for 3Dreconstruction from satellite imagery using deep learning.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

In the following description of preferred embodiments, the samereference numerals will be used for similar features in the differentdrawings. The drawings are not drawn to scale.

In FIG. 1 , a method for 3D reconstruction from satellite or spacebornimagery using deep learning is illustrated.

The method comprises a step of providing 101 at least two 2D satelliteimages. The at least two 2D satellite images may comprise imagescaptured within the visible wavelength field, Near Infrared, NIR, and/orthermal images. The images may be panchromatic or multispectral or acombination of all spectral bands.

The images are captured by an imaging device. The at least two satelliteimages comprise overlapping images.

The overlapping images may be bundle adjusted. Given a set of imagesdepicting a number of 3D points from different viewpoints, bundleadjustment can be defined as the problem of simultaneously refining the3D coordinates describing the scene geometry as well as the parametersof the relative motion and the optical characteristics of the camera(s)employed to acquire the images, according to an optimality criterioninvolving the corresponding image projections of all points.

Further, the 2D satellite images are associated to imaging deviceparameters relating to the time of capture of the respective image. Theimaging device parameters comprise extrinsic and/or intrinsicparameters. The imaging device extrinsic parameters comprise for examplea 3D position of the imaging device and/or a pointing direction of theimaging device and/or a rotational position of the imaging device.

The imaging device extrinsic parameters are for example represented in acamera model. The camera model is for example a rational polynomialcoefficient, RPC, model. The RPC model is a generalized sensor model,which can achieve high approximation accuracy. The RPC model is widelyused in the field of photogranimetry and remote sensing. In anotherexample, the camera model is a rigorous sensor model known in the art.These are only examples, there are many ways known in the art forproviding extrinsic and/or intrinsic imaging device parameters.

The method comprises further a step of providing 102 at least some ofthe intrinsic and/or extrinsic imaging device parameters for the atleast two 2D satellite images. Examples of instrinsic and/or extrinsicimaging parameters which may be provided are given above, wherein it isexplained that the 2D satellite images are associated to imaging deviceparameters relating to the time of capture of the respective image.

The method further comprises a step of providing 103 at least onetrained Machine Learning Network, MLN, able to predict depth maps. Thetrained MLN has been trained on a training set comprising multi-view 3Dgeocoded ground truth data. The term “3D geocoded ground truth data”refers to that the ground truth data is geocoded, i.e. comprises a3-dimensional coordinate.

The data of the training set may have a higher resolution than theresolution of the at least two 2D satellite images.

The multi-view 3D geocoded ground truth data may be rendered from ageocoded 3D surface model provided for training purposes for training ofthe MLN.

The multi-view 3D geocoded ground truth data may be real world data, incontrast to synthetic data. Alternatively, the ground truth data maycomprise both real world data and synthetic data. Alternatively, theground truth data may be synthetic data.

Accordingly, the multi-view 3D geocoded ground truth data may berendered from a geocoded 3D surface model provided for training of theMLN and provided based on real world data.

The multi-view 3D geocoded ground truth data may comprise images. Theimages may be captured from the ground or from the air, such as from anaircraft, drone etc. The images may be captured by an imaging devicesuch as a camera for visual light or by an IR or NIR camera or a cameraoperating in any other range.

Thus, multi-view 3D geocoded ground truth data may comprise or berendered from a 3D geocoded 3D surface model provided for training ofthe MLN and formed based on images.

The generation of a geocoded 3D surface model from 2D images is knownfrom the prior art and is described in, e.g., WO2014/112911. The methoddescribed in said publication comprises the steps of providing aplurality of overlapping images of the environment, each image beingassociated to geo-position data; optionally providing distanceinformation, said distance information comprising a distance value andgeo-position data from a plurality of distance measurements; anddeveloping the 3D model based on the plurality of overlapping images andthe distance information, if provided.

The step of developing the geocoded 3D surface model used for trainingthe MLN may comprise the steps of providing the geocoded 3D surfacemodel based on the plurality of overlapping images and updating thegeocoded 3D surface model with the distance information using aniterative process. There are a few different ways of representing thegeocoded 3D surface model. The geocoded 3D surface model may berepresented as a mesh, as a surface representation, or as a voxelrepresentation or as a point cloud.

The multi-view 3D geocoded ground truth data may comprise distancemeasurement data such as LIDAR measurement data and/or radar measurementdata and/or sonar measurement data and/or distance measurement dataprovided using structured light. For example, processing may beperformed of a plurality of distance measurements for a plurality ofgeographically known positions using a distance-determining device,thereby providing a geocoded 3D surface model for use as a training set.

The multi-view ground truth data may relate to a plurality ofgeographical areas and comprise a plurality of reference images and/ormeasurement data capturing each geographical area from different angles.The multi-viel ground truth data may have been extracted from a geocoded3D surface model.

The training set may relate to a built-up environment, such as an urbanenvironment. The built-up environment may be a residential area with lowbuildings, an area with medium-high buildings or a city center with highbuildings. The training set may instead relate to a mountainouslandscape. These examples relate to an environment which issubstantially constant over time, and which has an elevation profilewith steep slopes. However, other terrain types are also possible.

The method further comprises a step of predicting 104 a depth map of theprovided at least two 2D satellite images using the trained at least oneMLN and based on the corresponding imaging device parameters. Theprediction of the depth map may comprise predicting also an uncertaintymap relating to the depth map. Thus, the MLN then generates the depthmap and the related uncertainty map. The uncertainty map indicates anuncertainty from each value of the depth map. The prediction of thedepth map may comprise associating the data of the depth map to geocodedtwo-dimensional coordinate data.

As is clear from the above the trained MLN may be arranged to predictdepth maps for built-up environments and/or mountainous landscapes.

The MLN may comprise a plurality of MLNs and wherein decisions are takenbased on voting.

The method may further comprise a step of generating 105 a textured ornon-textured geocoded 3D surface model based on the predicted depth map.

The method may further comprise a pre-step of training 99 the at leastone MLN.

In FIG. 2 , processing of a computer program is illustrated forgeneration of data for 3D reconstruction from satellite imagery usingdeep learning according to at least some of the examples of the methodas disclosed in relation to FIG. 1 .

In FIG. 2 , at least two 2D satellite images 201 a, 202 a are providedto a trained MLN 204 a. Also imaging device parameters associated to therespective satellite image 201 a, 202 a are provided to the trained MLN204 a. The satellite images 201 a. 202 a and/or imaging deviceparameters 203 a may be stored in a memory (not disclosed) and obtainedtherefrom for processing by the MLN 204 a.

The trained MLN 204 a is arranged to predict a depth map 205 a of theprovided at least two 2D satellite images based on the at least two 2Dsatellite images with their corresponding imaging device parameters.

In the illustrated example, the trained MLN comprises a plurality oftrained MLNs 204 a, 204 b, 204 c. Each MLN is then arranged to eachgenerate a depth map 205 a, 205 b, 206 c. An updated depth map is thenformed based on the predicted depth maps. When available, the updateddepth map may further be formed based on an uncertainty map relating tothe respective depth map. An updated uncertainty map may then be formedaccordingly.

A fusion unit/function 206 for data fusion may then be arranged to formthe updated depth map 205 d to be used to represent the area covered bythe at least two 2D satellite images. In one example, one of the depthmaps 205 a, 205 b, 205 c is selected to represent the area covered bythe at least two 2D satellite images. Alternatively, the depth maps 205a, 205 b, 205 c generated by the MLNs are combined to provide the depthmap 205 d to be used to represent the area covered by the at least two2D satellite images. As mentioned above, in data fusion, one depth mapmay be selected (so-called voting) or a plurality of depth maps may becombined. The data fusion may utilize an uncertainty map associated tothe respective depth map.

There are many methods known in the art for fusion of data sets.Different statistical means can be used such as averaging, whereinoutliners may have been removed.

In one example, the trained MLNs are differently trained. The at leasttwo 2D satellite images 201 a, 202 a; 201 b, 202 b; 201 c, 202 cprocessed by the respective trained MLN may then be the same at leasttwo 2D satellite images for all MLNs. Alternatively, the plurality oftrained MLNs may be copies of the same MLN. In accordance with theillustrated example, the MLNs may be fed with different image sets. Forexample, the first MLN 204 a may be provided with a first set of 2Dsatellite images 201 a, 202 a, the second MLN 204 a may be provided witha second set of 2D satellite images 201 b, 202 b and the at least onethird MLN 204 c may be provided with at least one third set of 2Dsatellite images 201 c, 202 c.

Further, the predicted depth map 205 a provided by the MLN 204 or theupdated depth map 205 d provided by data fusion may be furtherprocessed.

There exists many ways of representing the geometry of a scene. ADigital Surface Model, DSM, is a type of 3D data used to describe theelevation of the Earth's surface. In a DSM, both artificial and naturalfeatures are captured. Artificial features include objects such ashouses and bridges, and natural features include vegetation. A DSM canbe represented using a grid of equally sized squares, commonly known asa raster. A DSM may also be represented using a triangular irregularnetwork as a mesh.

A point cloud is an unordered set of points in 3D space representing anobject or scene. It is common associating each point with extra datadepending on the application, e.g. a colour value for visualization. Inorder to speed up processing, it is common to partition the point cloudinto uniform voxels or into an octree.

Thus, the depth map 205 a, 205 d may be used for generating a geocoded3D surface model 207. The geocoded 3D surface model 207 may be generateddirectly from the depth map 205 a, 205 d. The depth map 205 a, 205 d maybe used to generate a point cloud 208. A geocoded 3D surface model 207may then be obtained based on the point cloud.

FIG. 4 shows schematically an illustrating example of data used forgeneration of data for 3D reconstruction from satellite images. In theillustrated example, images of an area on the ground 1 are capturedusing a first satellite 10 and a second satellite 11. Further, imageshave been taken using an aircraft 12. As us shown in FIG. 3 the firstsatellite 10 captures a first two-dimensional (2D) satellite image of afirst area 2 from a first angle and the second satellite 11 captures asecond two-dimensional (2D) satellite image of the first area 2 from asecond angle.

Also shown in FIG. 4 is an aircraft and yet another satellite, whichcapture images of a second geographical area 3 from different angles.Those images of the second geographical area may be used for trainingthe MLN. Preferably, the data used for training the MLN has a higherresolution than the images captured of the first area 2. Preferably, theimages captured from different angles of the second geographical areaare geocoded. Those images captured from different angles may have beenused in advance for forming a geocoded 3D surface model for trainingpurposes and then data may be extracted from this geocoded 3D surfacemodel to provide multi-view 3D geocoded ground truth data. Alternativelythose images captured from different images are directly used forproviding multi-view 3D geocoded ground truth data for use in thetraining.

In the illustrated example, due to the smaller distance between theaircraft and the ground 1 compared to the distance between thesatellites 11, 12, and the ground 1, it is possible to achieve a higherresolution on the images captured from the aircraft 13 than on theimages captured from the satellites.

In FIG. 4 the second geographical area 3 is within the first area 2.However, the second geographical area 3 may be situated at anotherlocation than within the first area 2.

However, this is only an example. For example, data obtained in otherways may form the training set. The data for the training set may beextracted from a geocoded 3D surface model provided for training the MLNand based on real world data. The data for the training set may compriseimages and/or LIDAR measurement data and/or radar measurement dataand/or sonar measurement data. The only thing that is important is thatthe training set comprises multi-view ground truth data. The trainingset may have been extracted from a geocoded 3D surface model. Thetraining set may relate to a plurality of geographical areas andcomprise a plurality of reference images and/or measurement datacapturing each geographical area from different angles.

In FIG. 3 , a method 300 for training a Machine Learning Network, MLN,for 3D reconstruction from satellite imagery is illustrated.

The method 300 comprises providing 301 at least one Machine LearningNetwork, MLN, able to predict depth maps.

The method 300 further comprising training 302 said MLN on a trainingset comprising at least one textured or non-textured geocoded 3D surfacemodel for training purposes, wherein multi-view ground truth data isextracted or rendered from the textured or non-textured geocoded 3Dsurface model. Thus, ground truth data relating to a geographic areaseen from at least two different views is rendered from the textured ornon-textured geocoded 3D surface model. Note that even if the geocoded3D surface model for training the MLN is textured, the trainer maychoose to not use the texture information.

In an example, the MLN is optimized based on a gradient decentoptimization algorithm. The gradient-based optimization algorithm mayfor example be a stochastic gradient descent optimizer or the Adamoptimizer.

FIG. 5 illustrates a 3D reconstruction from satellite imagery using deeplearning. The 3D reconstruction comprises geocoded reference data andtexture information on the lower right part of the 3D reconstruction andonly geocoded non-textured reference data from depth map(s) on the upperleft part.

There are a different ways of representing the 3D reconstruction fromsatellite imagery. The 3D reconstruction may for example be representedas a mesh, as a surface representation, or as a voxel representation oras a point cloud.

In the illustrated example, the 3D reconstruction is represented as amesh. A processor is arranged to form the mesh based on the depth map(s)as predicted herein. In detail, the processor may be arranged to formthe mesh by forming nodes interconnected by edges forming surfacesdefined by the edges, wherein each node is associated to athree-dimensional geocoded reference data in a geographical coordinatesystem. Further, texture information provided from the satellite imagesor obtained from other source(s) may be associated to the surfaces ofthe mesh.

In FIG. 6 , an example 3D reconstruction is formed as a mesh 600. Themesh 600 comprises a plurality of nodes 601 interconnected by means ofedges 602. Surfaces 603 are provided boarded by the edges 602 of themesh 600. The nodes 601 are each associated to a 3D coordinate in ageographical coordinate system. The surfaces 603 are in one example eachassociated to texture information. In one example, the surfaces are alsoeach associated to 3D coordinate data in the geographical coordinatesystem. Further, a mesh uncertainty is associated to at least a subsetof the nodes of the mesh. The mesh uncertainty associated to eachrespective node represents the uncertainty at that specific point of themodel. In one example, the mesh uncertainty is associated to each nodeof the mesh. Determination of a mesh uncertainty is for examplediscussed in WO2014/112908.

Instead, or in addition thereto, at least a subset of the surfacesand/or edges can be associated to a mesh uncertainty. In one example,one mesh uncertainty is associated to each surface and/or edge.Alternatively, each surface and/or edge is associated to a plurality ofmesh uncertainty values. For example, the mesh uncertainty values of theedges/surfaces are determined based on interpolation betweenneighbouring nodes.

In FIG. 7 , the mesh uncertainty is illustrated. A value 700 for themesh uncertainty is given in at least two directions. In the illustratedexample, the mesh uncertainty value 700 is given in two dimensions. Theuncertainty value in each direction is in one example represented as adistance or another value related to the distance. In one example, theuncertainty is represented as a value and possibly also direction in theplane of the surface and as a value in a direction perpendicular to theplane of the surface. In accordance with this example, each uncertaintyis represented in relation to the associated local plane given by thesurface of the mesh. When the uncertainty is given in space, theuncertainty defines an ellipsoid, the size and shape of which is givenby the uncertainty value in each respective direction. In one examplewhen the mesh uncertainty is given in three dimensions, it isrepresented as a 3×3 matrix. In one example when the mesh uncertainty isgiven in two dimensions, it is represented as a 2×2 matrix. Theuncertainty may be represented as a probability.

FIG. 8 shows a system for 3D reconstruction from satellite imagery usingdeep learning, which system is implemented on a server 800. The server800 comprises a first input 801 for 3D satellite images with associatedcamera parameters. The server also comprises a second input 802 for atleast one MLN or a training set for training said at least one MLN.

The server 800 comprises a processor 803. A computer program runs on theprocessor 803, which makes the server 200 to perform the method(s)according to the invention. Thus, the processor 803 may be arranged toperform the training of the MLN(s) or be provided with the trainedMLN(s). The processor may generate the 3D reconstruction from satelliteimagery as described above.

The server 800 further comprises a memory 804. The memory may bearranged to store the generated 3D reconstruction and/or satelliteimagery for 3D reconstruction, and or training data, and or at least oneMLN.

1. A method for 3D reconstruction from satellite imagery using deeplearning, said method comprising the steps of: providing (101) at leasttwo overlapping 2D satellite images, providing (102) imaging deviceparameters for the at least two overlapping 2D satellite images,providing (103) at least one trained Machine Learning Network, MLN, ableto predict depth maps, said trained MLN being trained on a training setcomprising multi-view 3D geocoded ground truth data, and predicting(104) a depth map of the at provided at least two 2D satellite imagesusing the trained at least one MLN and based on the correspondingimaging device parameters, wherein the prediction of the depth mapcomprises predicting also an uncertainty map relating to the depth map.2. The method according to claim 1, wherein the data of the training sethas higher resolution that the at least two 2D satellite images.
 3. Themethod according to claim 1, wherein the multi-view 3D geocoded groundtruth data is real world data.
 4. The method according to claim 1,wherein the multi-view 3D geocoded ground truth data is extracted from ageocoded 3D surface model for training purposes, said geocoded 3Dsurface model being provided based on real world data.
 5. The methodaccording to claim 1, wherein the multi-view 3D geocoded ground truthdata comprises images and/or LIDAR measurement data and/or radarmeasurement data and/or sonar measurement data.
 6. The method accordingto claim 1, wherein the overlapping 2D satellite images are bundleadjusted.
 7. The method according to claim 1, wherein the training setrelates to a built up environment.
 8. The method according to claim 1,wherein the multi-view 3D geocoded ground truth data relates to aplurality of geographical areas and comprises a plurality of referenceimages and/or measurement data capturing each geographical area fromdifferent angles.
 9. The method according to claim 1, wherein the groundtruth data is geocoded.
 10. The method according to claim 1, furthercomprising a step of training (99) the at least one MLN.
 11. (canceled)12. A method for 3D reconstruction from satellite imagery using deeplearning, said method comprising the steps of: providing (101) at leasttwo overlapping 2D satellite images, providing (102) imaging deviceparameters for the at least two overlapping 2D satellite images,providing (103) at least one trained Machine Learning Network, MLN, ableto predict depth maps, said trained MLN being trained on a training setcomprising multi-view 3D geocoded ground truth data, and predicting(104) a depth map of the at provided at least two 2D satellite imagesusing the trained at least one MLN and based on the correspondingimaging device parameters, wherein the MLN comprises a plurality of MLNseach predicting a depth map and wherein an updated depth map is formedbased on the predicted depth maps and when available based on theuncertainty map relating to the respective depth map.
 13. A method for3D reconstruction from satellite imagery using deep learning, saidmethod comprising the steps of: providing (101) at least two overlapping2D satellite images, providing (102) imaging device parameters for theat least two overlapping 2D satellite images, providing (103) at leastone trained Machine Learning Network, MLN, able to predict depth maps,said trained MLN being trained on a training set comprising multi-view3D geocoded ground truth data, and predicting (104) a depth map of theat provided at least two 2D satellite images using the trained at leastone MLN and based on the corresponding imaging device parameters,wherein the prediction of the depth map comprises associating the dataof the depth map to geocoded coordinate data.
 14. A method for 3Dreconstruction from satellite imagery using deep learning, said methodcomprising the steps of: providing (101) at least two overlapping 2Dsatellite images, providing (102) imaging device parameters for the atleast two overlapping 2D satellite images, providing (103) at least onetrained Machine Learning Network, MLN, able to predict depth maps, saidtrained MLN being trained on a training set comprising multi-view 3Dgeocoded ground truth data, predicting (104) a depth map of the atprovided at least two 2D satellite images using the trained at least oneMLN and based on the corresponding imaging device parameters, andgenerating (105) a geocoded 3D surface model based on the predicteddepth map.
 15. A method for 3D reconstruction from satellite imageryusing deep learning, said method comprising the steps of: providing(101) at least two overlapping 2D satellite images, providing (102)imaging device parameters for the at least two overlapping 2D satelliteimages, providing (103) at least one trained Machine Learning Network,MLN, able to predict depth maps, said trained MLN being trained on atraining set comprising multi-view 3D geocoded ground truth data, andpredicting (104) a depth map of the at provided at least two 2Dsatellite images using the trained at least one MLN and based on thecorresponding imaging device parameters, wherein the trained MLN isarranged to predict depth maps for built-up environments.
 16. A method(300) for training a Machine Learning Network, MLN, for 3Dreconstruction from satellite imagery, said method comprising the stepsof: providing (301) at least one trained Machine Learning Network, MLN,able to predict depth maps, and training (302) said MLN on a trainingset comprising at least one textured or non-textured 3D geocoded 3Dsurface model for training purposes, wherein multi-view 3D geocodedground truth data is rendered from said textured or non-texturedgeocoded 3D surface model for training purposes.
 17. A computer programfor 3D reconstruction from satellite imagery using deep learning,comprising instructions which, when executed by at least one processorcause the at least one processor to carry out the method according toclaim
 1. 18. A computer program for training a Machine Learning Network,MLN, for 3D reconstruction from satellite imagery, comprisinginstructions which, when executed by at least one processor cause the atleast one processor to carry out the method according to claim
 16. 19.The method according to claim 12, wherein one or more of: the data ofthe training set has higher resolution that the at least two 2Dsatellite images, the multi-view 3D geocoded ground truth data is realworld data, the multi-view 3D geocoded ground truth data is extractedfrom a geocoded 3D surface model for training purposes, said geocoded 3Dsurface model being provided based on real world data, the multi-view 3Dgeocoded ground truth data comprises images and/or LIDAR measurementdata and/or radar measurement data and/or sonar measurement data, theoverlapping 2D satellite images are bundle adjusted, the training setrelates to a built up environment, the multi-view 3D geocoded groundtruth data relates to a plurality of geographical areas and comprises aplurality of reference images and/or measurement data capturing eachgeographical area from different angles, or the ground truth data isgeocoded.
 20. The method according to claim 13, wherein one or more of:the data of the training set has higher resolution that the at least two2D satellite images, the multi-view 3D geocoded ground truth data isreal world data, the multi-view 3D geocoded ground truth data isextracted from a geocoded 3D surface model for training purposes, saidgeocoded 3D surface model being provided based on real world data, themulti-view 3D geocoded ground truth data comprises images and/or LIDARmeasurement data and/or radar measurement data and/or sonar measurementdata, the overlapping 2D satellite images are bundle adjusted, thetraining set relates to a built up environment, the multi-view 3Dgeocoded ground truth data relates to a plurality of geographical areasand comprises a plurality of reference images and/or measurement datacapturing each geographical area from different angles, or the groundtruth data is geocoded.
 21. The method according to claim 14, whereinone or more of: the data of the training set has higher resolution thatthe at least two 2D satellite images, the multi-view 3D geocoded groundtruth data is real world data, the multi-view 3D geocoded ground truthdata is extracted from a geocoded 3D surface model for trainingpurposes, said geocoded 3D surface model being provided based on realworld data, the multi-view 3D geocoded ground truth data comprisesimages and/or LIDAR measurement data and/or radar measurement dataand/or sonar measurement data, the overlapping 2D satellite images arebundle adjusted, the training set relates to a built up environment, themulti-view 3D geocoded ground truth data relates to a plurality ofgeographical areas and comprises a plurality of reference images and/ormeasurement data capturing each geographical area from different angles,or the ground truth data is geocoded.
 22. The method according to claim15, wherein one or more of: the data of the training set has higherresolution that the at least two 2D satellite images, the multi-view 3Dgeocoded ground truth data is real world data, the multi-view 3Dgeocoded ground truth data is extracted from a geocoded 3D surface modelfor training purposes, said geocoded 3D surface model being providedbased on real world data, the multi-view 3D geocoded ground truth datacomprises images and/or LIDAR measurement data and/or radar measurementdata and/or sonar measurement data, the overlapping 2D satellite imagesare bundle adjusted, the training set relates to a built up environment,the multi-view 3D geocoded ground truth data relates to a plurality ofgeographical areas and comprises a plurality of reference images and/ormeasurement data capturing each geographical area from different angles,or the ground truth data is geocoded.